class_docstr: Trainer using distinct optimizers for encoder and decoder nn.
  
  Args:
      model (BaseAE): The model to train
  
      train_dataset (BaseDataset): The training dataset of type
          :class:`~pythae.data.dataset.BaseDataset`
  
      training_args (CoupledOptimizerTrainerConfig): The training arguments summarizing the main
          parameters used for training. If None, a basic training instance of
          :class:`CoupledOptimizerTrainerConfig` is used. Default: None.
  
      encoder_optimizer (~torch.optim.Optimizer): An instance of `torch.optim.Optimizer` used for
          training the encoder. If None, a :class:`~torch.optim.Adam` optimizer is used.
          Default: None.
  
      decoder_optimizer (~torch.optim.Optimizer): An instance of `torch.optim.Optimizer` used for
          training the decoder. If None, a :class:`~torch.optim.Adam` optimizer is used.
          Default: None.
docstr: This function is the main training function
  
  Args:
      log_output_dir (str): The path in which the log will be stored
docstr_tok: ["This","function","is","the","main","training","function","Args",":","log_output_dir","(","str",")",":","The","path","in","which","the","log","will","be","stored"]
code: def train(self, log_output_dir: str = None):
      """This function is the main training function
  
      Args:
          log_output_dir (str): The path in which the log will be stored
      """
  
      self.prepare_training()
  
      self.callback_handler.on_train_begin(
          training_config=self.training_config,
          model_config=self.model_config,
      )
  
      log_verbose = False
  
      msg = (
          f"Training params:\n - max_epochs: {self.training_config.num_epochs}\n"
          " - per_device_train_batch_size: "
          f"{self.training_config.per_device_train_batch_size}\n"
          " - per_device_eval_batch_size: "
          f"{self.training_config.per_device_eval_batch_size}\n"
          f" - checkpoint saving every: {self.training_config.steps_saving}\n"
          f"Encoder Optimizer: {self.encoder_optimizer}\n"
          f"Encoder Scheduler: {self.encoder_scheduler}\n"
          f"Decoder Optimizer: {self.decoder_optimizer}\n"
          f"Decoder Scheduler: {self.decoder_scheduler}\n"
      )
  
      if self.is_main_process:
          logger.info(msg)
  
      # set up log file
      if log_output_dir is not None and self.is_main_process:
          log_verbose = True
  
          file_logger = self._get_file_logger(
              log_output_dir=log_output_dir
          )
  
          file_logger.info(msg)
  
      if self.is_main_process:
          logger.info("Successfully launched training !\n")
  
      # set best losses for early stopping
      best_train_loss = 1e10
      best_eval_loss = 1e10
  
      for epoch in range(1, self.training_config.num_epochs + 1):
          self.callback_handler.on_epoch_begin(
              training_config=self.training_config,
              epoch=epoch,
              train_loader=self.train_loader,
              eval_loader=self.eval_loader,
          )
  
          metrics = {}
  
          train_losses = self.train_step(epoch)
  
          [
              epoch_train_loss,
              epoch_train_encoder_loss,
              epoch_train_decoder_loss,
          ] = train_losses
          metrics["train_epoch_loss"] = epoch_train_loss
          metrics["train_encoder_loss"] = epoch_train_encoder_loss
          metrics["train_decoder_loss"] = epoch_train_decoder_loss
  
          if self.eval_dataset is not None:
              eval_losses = self.eval_step(epoch)
  
              [
                  epoch_eval_loss,
                  epoch_eval_encoder_loss,
                  epoch_eval_decoder_loss,
              ] = eval_losses
              metrics["eval_epoch_loss"] = epoch_eval_loss
              metrics["eval_encoder_loss"] = epoch_eval_encoder_loss
              metrics["eval_decoder_loss"] = epoch_eval_decoder_loss
  
              self._schedulers_step(
                  encoder_metrics=epoch_eval_encoder_loss,
                  decoder_metrics=epoch_eval_decoder_loss,
              )
  
          else:
              epoch_eval_loss = best_eval_loss
              self._schedulers_step(
                  encoder_metrics=epoch_train_encoder_loss,
                  decoder_metrics=epoch_train_decoder_loss,
              )
  
          if (
              epoch_eval_loss < best_eval_loss
              and not self.training_config.keep_best_on_train
          ):
              best_eval_loss = epoch_eval_loss
              best_model = deepcopy(self.model)
              self._best_model = best_model
  
          elif (
              epoch_train_loss < best_train_loss
              and self.training_config.keep_best_on_train
          ):
              best_train_loss = epoch_train_loss
              best_model = deepcopy(self.model)
              self._best_model = best_model
  
          if (
              self.training_config.steps_predict is not None
              and epoch % self.training_config.steps_predict == 0
              and self.is_main_process
          ):
              true_data, reconstructions, generations = self.predict(
                  best_model
              )
  
              self.callback_handler.on_prediction_step(
                  self.training_config,
                  true_data=true_data,
                  reconstructions=reconstructions,
                  generations=generations,
                  global_step=epoch,
              )
  
          self.callback_handler.on_epoch_end(
              training_config=self.training_config
          )
  
          # save checkpoints
          if (
              self.training_config.steps_saving is not None
              and epoch % self.training_config.steps_saving == 0
          ):
              if self.is_main_process:
                  self.save_checkpoint(
                      model=best_model,
                      dir_path=self.training_dir,
                      epoch=epoch,
                  )
                  logger.info(f"Saved checkpoint at epoch {epoch}\n")
  
                  if log_verbose:
                      file_logger.info(
                          f"Saved checkpoint at epoch {epoch}\n"
                      )
  
          self.callback_handler.on_log(
              self.training_config,
              metrics,
              logger=logger,
              global_step=epoch,
              rank=self.rank,
          )
  
      final_dir = os.path.join(self.training_dir, "final_model")
  
      if self.is_main_process:
          self.save_model(best_model, dir_path=final_dir)
          logger.info("----------------------------------")
          logger.info("Training ended!")
          logger.info(f"Saved final model in {final_dir}")
  
      if self.distributed:
          dist.destroy_process_group()
  
      self.callback_handler.on_train_end(self.training_config)
  
imports: import datetime
  import logging
  import os
  from copy import deepcopy
  from typing import List, Optional
  import torch
  import torch.distributed
  import torch.optim
  import torch.optim.lr_scheduler
  from ...data.datasets import BaseDataset
  from ...models import BaseAE
  from ..base_trainer import BaseTrainer
  from ..trainer_utils import set_seed
  from ..training_callbacks import TrainingCallback
  from .coupled_optimizer_trainer_config import CoupledOptimizerTrainerConfig
label_desc: Use image node_id 9 for calling the CoupledOptimizerTrainer obj's underlying member method code with example usage: obj.train(log_output_dir) without return types